Collaborative Decision-Making and Optimal Utilization of Pathfinding Flights during Convective Weather

TL;DR

This paper presents a comprehensive analytical framework for modeling and optimizing pathfinder operations during convective weather, incorporating weather uncertainty, flight responses, and offer sequencing to improve air traffic capacity and reduce delays.

Contribution

It introduces a unified model combining weather-driven system states, decision-making, and offer sequencing, providing the first formal decision models for pathfinder operations under uncertainty.

Findings

Distinct sequencing strategies emerge from ATC and dispatcher objectives.

Steady-state analysis reveals long-term capacity impacts.

Simulation demonstrates improved decision-making under weather uncertainty.

Abstract

Air traffic operations are strongly influenced by convective weather, and one common response is pathfinder operations, in which a designated aircraft tests the viability of weather-impacted airspace and routes. Despite relatively routine use in practice, how pathfinder operations evolve under uncertainty and how the pathfinder decision-making process unfolds are largely treated as exogenous. Addressing this gap requires jointly modeling weather-driven system accessibility, flight responses to pathfinder offers, and the sequencing of those offers to improve outcomes. We develop a unified analytical framework that connects weather-driven system state transitions, flight acceptance decisions, and the sequencing of pathfinder offers. We first construct a four-state Markov chain to model stochastic closure and reopening of exit points, or fixes, out of the terminal departure airspace surrounding a major airport, pathfinder selection, and pathfinding execution, and analyze its steady-state behavior to characterize long-term capacity and delay implications. We introduce utility-based decision models for flights, air traffic control (ATC), and dispatchers, and analyze worst-case collective rejection to quantify system vulnerability under selfless behavior and uncertainty. Finally, we formulate optimization problems that model ATC-initiated and dispatcher-initated pathfinder offers, with the goal of optimizing the sequence of pathfinder offers. Using a discrete event simulation for a major US airport, we show that ATC- and dispatcher-driven objectives lead to distinct, near real-time sequencing strategies, providing the first formal decision models for pathfinder operations under weather uncertainty.